2 research outputs found
Facilitating constructivist e-learning by software agents
University of Technology, Sydney. Faculty of Information Technology.E-learning is being taken as an important means to satisfy the increasing demands for learning in today’s information society. Although considerable research effort has been devoted to facilitating e-learning, very little has been done to support constructivist e-leaming. This research attempts to develop an online constructivist learning environment (CLE) and utilize software agents to provide supportive services for learners to facilitate and assist them to build knowledge by using constructivist ways.
Constructivists assume knowledge is constructed by learners. Learners are knowledge-constructors whereas teachers are facilitators for the construction. Constructivist learning theory provides a framework to develop an online CLE. The important issues are concerned with what supportive services should be provided for learners and how to provide these services.
The services identified in the work include:
• providing access to appropriate learning resources and learning strategies;
• fostering meaningful interactions with content, teachers, and fellow learners;
• supporting personalized learning for individual learners;
• facilitating collaborative learning among learners in groups; and
• aiding to timely evaluate learning outcomes.
An innovative strategy is adopted to organize these services. They are provided for learners in non-intrusive ways. Learners are not forced to accept any of the services. They can autonomously take control over their learning. Meanwhile, they are offered services through suggestion or advice. These spontaneous services help them solve various possible problems in learning and assist them to progress in the online learning process.
All the supportive services are adapted to individual learners. Three key adaptations, service content, presentation manner, and intervention degree, are applied. Profiles are built to characterize individual learning characteristics, including knowledge constitution, cognition ability, and learning styles. All the services are dynamically generated based on actual learning scenes. A learning process specification language, built upon Koper’s EML, is developed to describe the learning activities and processes and the corresponding supportive services.
A new type of agents, process agents, is developed to realize the services. Three classes of agents, personal assistant agent, planning agent, and managing agent, have been incorporated into the learning environment to provide support for learners. They work in the background, monitor and evaluate individual learner’s learning, and provide supportive services for learners whenever necessary. Together they play a role of ''constructivist teacher".
To demonstrate the work, a system prototype has been developed and a number of services have been implemented. A preliminary evaluation has illustrated the agent-based approach can facilitate construction of knowledge by individual learners
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Adaptive Synchronization of Semantically Compressed Instructional Videos for Collaborative Distance Learning
The increasing popularity of online courses has highlighted the need for collaborative learning tools for student groups. In addition, the introduction of lecture videos into the online curriculum has drawn attention to the disparity in the network resources available to students. We present an e-Learning architecture and adaptation model called AI2TV (Adaptive Interactive Internet Team Video), which allows groups of students to collaboratively view a video in synchrony. AI2TV upholds the invariant that each student will view semantically equivalent content at all times. A semantic compression model is developed to provide instructional videos at different level-of-details to accommodate dynamic network conditions and usersäó» system requirements. We take advantage of the semantic compression algorithmäó»s ability to provide different layers of semantically equivalent video by adapting the client to play at the appropriate layer that provides the client with the richest possible viewing experience. Video player actions, like play, pause and stop, can be initiated by any group member and and the results of those actions are synchronized with all the other students. These features allow students to review a lecture video in tandem, facilitating the learning process. Experimental trials show that AI2TV successfully synchronizes instructional videos for distributed students while concurrently optimizing the video quality, even under conditions of fluctuating bandwidth, by adaptively adjusting the quality level for each student while still maintaining the invariant